--- title: "Papers of 2023 — page 5" description: "Papers of 2023: 769 papers of the Elamur Research library — what each work is about in our own words, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/papers/2023/page/5/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/2023/page/5/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / 2023 # Papers of 2023 — page 5 769 papers · [all years](https://md.research.elamur.ai/free/en/papers/) [← prev](https://md.research.elamur.ai/free/en/papers/2023/page/4/) [1](https://md.research.elamur.ai/free/en/papers/2023/) … [4](https://md.research.elamur.ai/free/en/papers/2023/page/4/) **5** [6](https://md.research.elamur.ai/free/en/papers/2023/page/6/) [7](https://md.research.elamur.ai/free/en/papers/2023/page/7/) [8](https://md.research.elamur.ai/free/en/papers/2023/page/8/) [next →](https://md.research.elamur.ai/free/en/papers/2023/page/6/) - [LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models](https://md.research.elamur.ai/free/en/papers/lin-2023-llm-eval-unified-multi-dimensional/) — Yen-Ting Lin, Yun-Nung Chen · arXiv:2305.13711 - [LLM-Pruner: On the Structural Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-llm-pruner-structural-pruning-large/) — Xinyin Ma, Gongfan Fang, Xinchao Wang · arXiv:2305.11627 - [LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2023-llmlingua-compressing-prompts-accelerated-inference/) — Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, et al. · Proceedings of the 2023 Conference on Empirical… - [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://md.research.elamur.ai/free/en/papers/zheng-2023-lmsys-chat-1m-large-scale/) — Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al. · arXiv:2309.11998 - [Locally Typical Sampling](https://md.research.elamur.ai/free/en/papers/meister-2023-locally-typical-sampling/) — Clara Meister, Tiago Pimentel, Gian Wiher, et al. · Transactions of the Association for… - [Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning](https://md.research.elamur.ai/free/en/papers/pan-2023-logic-lm-empowering-large-language/) — Liangming Pan, Alon Albalak, Xinyi Wang, et al. · arXiv:2305.12295 - [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://md.research.elamur.ai/free/en/papers/bai-2023-longbench-bilingual-multitask-benchmark/) — Yushi Bai, Xin Lv, Jiajie Zhang, et al. · arXiv:2308.14508 - [LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization](https://md.research.elamur.ai/free/en/papers/krishna-2023-longeval-guidelines-human-evaluation-faithfulness/) — Kalpesh Krishna, Erin Bransom, Bailey Kuehl, et al. · Proceedings of the 17th Conference of the… - [LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://md.research.elamur.ai/free/en/papers/jiang-2023-longllmlingua-accelerating-enhancing-llms-long/) — Huiqiang Jiang, Qianhui Wu, Xufang Luo, et al. · arXiv:2310.06839 - [LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2023-longlora-efficient-fine-tuning-long/) — Yukang Chen, Shengju Qian, Haotian Tang, et al. · arXiv:2309.12307 - [LooGLE: Can Long-Context Language Models Understand Long Contexts?](https://md.research.elamur.ai/free/en/papers/li-2023-loogle-long-context-language-models/) — Jiaqi Li, Mengmeng Wang, Zilong Zheng, et al. · arXiv:2311.04939 - [Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/guerreiro-2023-looking-needle-haystack-comprehensive-study/) — Nuno M. Guerreiro, Elena Voita, André Martins · Proceedings of the 17th Conference of the… - [Lost in the Middle: How Language Models Use Long Contexts](https://md.research.elamur.ai/free/en/papers/liu-2023-lost-middle-language-models-use/) — Nelson F. Liu, Kevin Lin, John Hewitt, et al. · arXiv:2307.03172 - [Low-Resource Languages Jailbreak GPT-4](https://md.research.elamur.ai/free/en/papers/yong-2023-low-resource-languages-jailbreak-gpt/) — Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach · arXiv:2310.02446 - [Machine Learning for Ancient Languages: A Survey](https://md.research.elamur.ai/free/en/papers/sommerschield-2023-machine-learning-ancient-languages-survey/) — Thea Sommerschield, Yannis Assael, John Pavlopoulos, et al. · Computational Linguistics - [Machine Translation for Ge'ez Language](https://md.research.elamur.ai/free/en/papers/wassie-2023-machine-translation-ge-ez-language/) — Aman Kassahun Wassie · arXiv:2311.14530 - [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://md.research.elamur.ai/free/en/papers/kudugunta-2023-madlad-400-multilingual-document-level/) — Sneha Kudugunta, Isaac Caswell, Biao Zhang, et al. · arXiv:2309.04662 - [Making Language Models Better Reasoners with Step-Aware Verifier](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/) — Yifei Li, Zeqi Lin, Shizhuo Zhang, et al. · Proceedings of the 61st Annual Meeting of the… - [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://md.research.elamur.ai/free/en/papers/yoran-2023-making-retrieval-augmented-language-models/) — Ori Yoran, Tomer Wolfson, Ori Ram, et al. · arXiv:2310.01558 - [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://md.research.elamur.ai/free/en/papers/yue-2023-mammoth-building-math-generalist-models/) — Xiang Yue, Xingwei Qu, Ge Zhang, et al. · arXiv:2309.05653 - [Mapping AMR to UMR: Resources for Adapting Existing Corpora for Cross-Lingual Compatibility](https://md.research.elamur.ai/free/en/papers/bonn-2023-mapping-amr-umr-resources-adapting/) — Julia Bonn, Skatje Myers, Jens E. L. Van Gysel, et al. · Machine translation - [Mapping Explicit and Implicit Discourse Relations between the RST-DT and the PDTB 3.0](https://md.research.elamur.ai/free/en/papers/costa-2023-mapping-explicit-implicit-discourse-relations/) — Nelson Filipe Costa, Nadia Sheikh, Leila Kosseim · English - [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://md.research.elamur.ai/free/en/papers/cheng-2023-marked-personas-natural-language-prompts/) — Myra Cheng, Esin Durmus, Dan Jurafsky · Proceedings of the 61st Annual Meeting of the… - [MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2023-mathcoder-seamless-code-integration-llms/) — Ke Wang, Houxing Ren, Aojun Zhou, et al. · arXiv:2310.03731 - [MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://md.research.elamur.ai/free/en/papers/macina-2023-mathdial-dialogue-tutoring-dataset-rich/) — Jakub Macina, Nico Daheim, Sankalan Chowdhury, et al. · Findings of the Association for Computational… - [Mathematical Capabilities of ChatGPT](https://md.research.elamur.ai/free/en/papers/frieder-2023-mathematical-capabilities-chatgpt/) — Simon Frieder, Luca Pinchetti, Alexis Chevalier, et al. · arXiv:2301.13867 · NeurIPS 2023 Datasets and… - [Mathematical Discoveries from Program Search with Large Language Models](https://md.research.elamur.ai/free/en/papers/paredes-2023-mathematical-discoveries-program-search-large/) — Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, et al. · Nature 625 - [MathPrompter: Mathematical Reasoning using Large Language Models](https://md.research.elamur.ai/free/en/papers/imani-2023-mathprompter-mathematical-reasoning-large-language/) — Shima Imani, Liang Du, Harsh Shrivastava · Proceedings of the 61st Annual Meeting of the… - [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://md.research.elamur.ai/free/en/papers/lu-2023-mathvista-evaluating-mathematical-reasoning-foundation/) — Pan Lu, Hritik Bansal, Tony Xia, et al. · arXiv:2310.02255 - [MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods](https://md.research.elamur.ai/free/en/papers/finkelstein-2023-mbr-qe-finetuning-training-time/) — Mara Finkelstein; Subhajit Naskar; Mehdi Mirzazadeh; Apurva Shah;, Markus Freitag · arXiv:2309.10966 - [Meaning Representation of English Prepositional Phrase Roles: SNACS Supersenses vs. Tectogrammatical Functors](https://md.research.elamur.ai/free/en/papers/scivetti-2023-meaning-representation-english-prepositional-phrase/) — Wesley Scivetti, Nathan Schneider · English - [Measuring Attribution in Natural Language Generation Models](https://md.research.elamur.ai/free/en/papers/rashkin-2023-measuring-attribution-natural-language-generation/) — Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, et al. · Computational Linguistics - [Measuring Faithfulness in Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/lanham-2023-measuring-faithfulness-chain-thought-reasoning/) — Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al. · arXiv:2307.13702 - [Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation](https://md.research.elamur.ai/free/en/papers/wein-2023-measuring-fine-grained-semantic-equivalence/) — Shira Wein, Zhuxin Wang, Nathan Schneider · Machine translation - [Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://md.research.elamur.ai/free/en/papers/pal-2023-med-halt-medical-domain-hallucination/) — Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu · arXiv:2307.15343 - [MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data](https://md.research.elamur.ai/free/en/papers/han-2023-medalpaca-open-source-collection-medical/) — Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, et al. · arXiv:2304.08247 - [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2023-meditron-70b-scaling-medical-pretraining/) — Zeming Chen, Alejandro Hernandez Cano, Angelika Romanou, et al. · arXiv:2311.16079 - [MEGA: Multilingual Evaluation of Generative AI](https://md.research.elamur.ai/free/en/papers/ahuja-2023-mega-multilingual-evaluation-generative-ai/) — Kabir Ahuja, Harshita Diddee, Rishav Hada, et al. · arXiv:2303.12528 - [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://md.research.elamur.ai/free/en/papers/hong-2023-metagpt-meta-programming-multi/) — Sirui Hong, Mingchen Zhuge, Jiaqi Chen, et al. · arXiv:2308.00352 - [MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/) — Longhui Yu, Weisen Jiang, Han Shi, et al. · arXiv:2309.12284 - [MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task](https://md.research.elamur.ai/free/en/papers/juraska-2023-metricx-23-google-submission-wmt/) — Juraj Juraska, Mara Finkelstein, Daniel Deutsch, et al. · Proceedings of the Eighth Conference on Machine… - [MIMIC-IV, a freely accessible electronic health record dataset](https://md.research.elamur.ai/free/en/papers/johnson-2023-mimic-iv-freely-accessible-electronic/) — Alistair E. W. Johnson, Lucas Bulgarelli, Lu Shen, et al. · Scientific Data - [Mind2Web: Towards a Generalist Agent for the Web](https://md.research.elamur.ai/free/en/papers/deng-2023-mind2web-towards-generalist-agent-web/) — Xiang Deng, Yu Gu, Boyuan Zheng, et al. · arXiv:2306.06070 - [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2023-minigpt-4-enhancing-vision-language/) — Deyao Zhu, Jun Chen, Xiaoqian Shen, et al. · arXiv:2304.10592 - [MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning](https://md.research.elamur.ai/free/en/papers/chen-2023-minigpt-v2-large-language-model/) — Jun Chen, Deyao Zhu, Xiaoqian Shen, et al. · arXiv:2310.09478 - [MiniLLM: On-Policy Distillation of Large Language Models](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/) — Yuxian Gu, Li Dong, Furu Wei, et al. · arXiv:2306.08543 - [MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback](https://md.research.elamur.ai/free/en/papers/wang-2023-mint-evaluating-llms-multi-turn/) — Xingyao Wang, Zihan Wang, Jiateng Liu, et al. · arXiv:2309.10691 - [MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages](https://md.research.elamur.ai/free/en/papers/zhang-2023-miracl-multilingual-retrieval-dataset-covering/) — Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, et al. · Research library - [Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-mitigating-hallucination-large-multi-modal/) — Fuxiao Liu, Kevin Lin, Linjie Li, et al. · arXiv:2306.14565 - [Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding](https://md.research.elamur.ai/free/en/papers/leng-2023-mitigating-object-hallucinations-large-vision/) — Sicong Leng, Hang Zhang, Guanzheng Chen, et al. · arXiv:2311.16922 - [MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation](https://md.research.elamur.ai/free/en/papers/huang-2023-mlagentbench-evaluating-language-agents/) — Qian Huang, Jian Vora, Percy Liang, et al. · arXiv:2310.03302 - [MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities](https://md.research.elamur.ai/free/en/papers/yu-2023-mm-vet-evaluating-large-multimodal/) — Weihao Yu, Zhengyuan Yang, Linjie Li, et al. · arXiv:2308.02490 - [MMBench: Is Your Multi-modal Model an All-around Player?](https://md.research.elamur.ai/free/en/papers/liu-2023-mmbench-multi-modal-model-all/) — Yuan Liu, Haodong Duan, Yuanhan Zhang, et al. · arXiv:2307.06281 - [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/fu-2023-mme-comprehensive-evaluation-benchmark-multimodal/) — Chaoyou Fu, Peixian Chen, Yunhang Shen, et al. · arXiv:2306.13394 - [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://md.research.elamur.ai/free/en/papers/yue-2023-mmmu-massive-multi-discipline-multimodal/) — Xiang Yue, Yuansheng Ni, Kai Zhang, et al. · arXiv:2311.16502 - [MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System](https://md.research.elamur.ai/free/en/papers/qin-2023-mmsd2-0-reliable-multi-modal/) — Libo Qin, Shijue Huang, Qiguang Chen, et al. · Findings of the Association for Computational… - [Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs](https://md.research.elamur.ai/free/en/papers/ge-2023-model-tells-discard-adaptive-kv/) — Suyu Ge, Yunan Zhang, Liyuan Liu, et al. · arXiv:2310.01801 - [Modeling Cross-Cultural Pragmatic Inference with Codenames Duet](https://md.research.elamur.ai/free/en/papers/shaikh-2023-modeling-cross-cultural-pragmatic-inference/) — Omar Shaikh, Caleb Ziems, William Held, et al. · Findings of the Association for Computational… - [Morphological Inflection: A Reality Check](https://md.research.elamur.ai/free/en/papers/lex-2023-kodner-inflection-reality-check/) — Jordan Kodner, Sarah Payne, Salam Khalifa, et al. · English - [Morphosyntactic aspects of agrammatism in Palestinian Arabic: Findings from a Semitic language](https://md.research.elamur.ai/free/en/papers/adam-2023-morphosyntactic-aspects-agrammatism-palestinian-arabic/) — Hisham Adam · Revista de Investigación en Logopedia 13 - [mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration](https://md.research.elamur.ai/free/en/papers/ye-2023-mplug-owl2-revolutionizing-multi-modal/) — Qinghao Ye, Haiyang Xu, Jiabo Ye, et al. · arXiv:2311.04257 - [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://md.research.elamur.ai/free/en/papers/ye-2023-mplug-owl-modularization-empowers-large/) — Qinghao Ye, Haiyang Xu, Guohai Xu, et al. · arXiv:2304.14178 - [Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation](https://md.research.elamur.ai/free/en/papers/jing-2023-multi-source-semantic-graph-based/) — Liqiang Jing, Xuemeng Song, Kun Ouyang, et al. · Proceedings of the 61st Annual Meeting of the… - [Multi-step Jailbreaking Privacy Attacks on ChatGPT](https://md.research.elamur.ai/free/en/papers/li-2023-multi-step-jailbreaking-privacy-attacks/) — Haoran Li, Dadi Guo, Wei Fan, et al. · Findings of the Association for Computational… - [Multi-VALUE: A Framework for Cross-Dialectal English NLP](https://md.research.elamur.ai/free/en/papers/ziems-2023-multi-value-framework-cross-dialectal/) — Caleb Ziems, William Held, Jingfeng Yang, et al. · English - [Multilingual Coreference Resolution in Multiparty Dialogue](https://md.research.elamur.ai/free/en/papers/zheng-2023-multilingual-coreference-resolution-multiparty-dialogue/) — Boyuan Zheng, Patrick Xia, Mahsa Yarmohammadi, et al. · Research library - [Multilingual Jailbreak Challenges in Large Language Models](https://md.research.elamur.ai/free/en/papers/deng-2023-multilingual-jailbreak-challenges-large-language/) — Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, et al. · arXiv:2310.06474 - [Multilingual Sequence-to-Sequence Models for Hebrew NLP](https://md.research.elamur.ai/free/en/papers/findings-2023-eyal-multilingual-sequence-sequence-models-hebrew/) — Matan Eyal, Hila Noga, Roee Aharoni, et al. · Modern Hebrew - [Multimodal Chain-of-Thought Reasoning in Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-multimodal-chain-thought-reasoning-language/) — Zhuosheng Zhang, Aston Zhang, Mu Li, et al. · arXiv:2302.00923 - [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/li-2023-mvbench-comprehensive-multi-modal-video/) — Kunchang Li, Yali Wang, Yinan He, et al. · arXiv:2311.17005 - [MWE as WSD: Solving Multiword Expression Identification with Word Sense Disambiguation](https://md.research.elamur.ai/free/en/papers/tanner-2023-mwe-as-wsd-solving-multiword/) — Joshua Tanner, Jacob Hoffman · Findings of the Association for Computational… - [MyVoice: Arabic Speech Resource Collaboration Platform](https://md.research.elamur.ai/free/en/papers/elshahawy-2023-myvoice-arabic-speech-resource-collaboration/) — Yousseif Elshahawy; Yassine El Kheir; Shammur Absar Chowdhury;, Ahmed Ali · arXiv:2308.02503 - [N-Shot Benchmarking of Whisper on Diverse Arabic Speech Recognition](https://md.research.elamur.ai/free/en/papers/talafha-2023-n-shot-benchmarking-whisper-diverse/) — Bashar Talafha, Abdul Waheed, Muhammad Abdul-Mageed · arXiv:2306.02902 - [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://md.research.elamur.ai/free/en/papers/wang-2023-neural-codec-language-models-zero/) — Chengyi Wang, Sanyuan Chen, Yu Wu, et al. · arXiv:2301.02111 - [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://md.research.elamur.ai/free/en/papers/dhole-2023-nl-augmenter-framework-task-sensitive/) — Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, et al. · Northern European Journal of Language Technology - [NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://md.research.elamur.ai/free/en/papers/sainz-2023-nlp-evaluation-trouble-need-measure/) — Oscar Sainz, Jon Campos, Iker García-Ferrero, et al. · Findings of the Association for Computational… - [Noor-Ghateh: A Benchmark Dataset for Evaluating Arabic Word Segmenters in Hadith Domain](https://md.research.elamur.ai/free/en/papers/alshuhayeb-2023-noor-ghateh-benchmark-dataset-evaluating/) — Huda AlShuhayeb, Behrouz Minaei-Bidgoli, Mohammad E. Shenassa, et al. · arXiv:2307.09630 - [Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting](https://md.research.elamur.ai/free/en/papers/huang-2023-all-languages-created-equal-llms/) — Haoyang Huang, Tianyi Tang, Dongdong Zhang, et al. · arXiv:2305.07004 - [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://md.research.elamur.ai/free/en/papers/greshake-2023-ve-signed-up-compromising-real/) — Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, et al. · arXiv:2302.12173 - [Nothing New Under the Sun? The Study of Biblical Hebrew in the Era of Generative Pre-trained AI](https://md.research.elamur.ai/free/en/papers/hbo-2023-elrod-nothing-new-under-sun-study/) — A. G. Elrod · Ancient Hebrew - [Nâbra: Syrian Arabic Dialects with Morphological Annotations](https://md.research.elamur.ai/free/en/papers/aclbulk-2023-nayouf-nabra-syrian-arabic-dialects-morphological/) — Amal Nayouf, Tymaa Hammouda, Mustafa Jarrar, et al. · Semitic languages - [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://md.research.elamur.ai/free/en/papers/lauren-2023-ccon-obelics-open-web-scale-filtered/) — Hugo Laurençon, Lucile Saulnier, Leo Tronchon, et al. · arXiv:2306.16527 - [OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/liu-2023-ocrbench-hidden-mystery-ocr-large/) — Yuliang Liu, Zhang Li, Mingxin Huang, et al. · arXiv:2305.07895 - [Offensive Hebrew Corpus and Detection using BERT](https://md.research.elamur.ai/free/en/papers/langstream-2023-hamad-offensive-hebrew-corpus-detection-using/) — Nagham Hamad, Mustafa Jarrar, Mohammad Khalilia, et al. · Modern Hebrew - [Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/lai-2023-okapi-instruction-tuned-large-language/) — Viet Lai, Chien Nguyen, Nghia Ngo, et al. · Proceedings of the 2023 Conference on Empirical… - [OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models](https://md.research.elamur.ai/free/en/papers/shao-2023-omniquant-omnidirectionally-calibrated-quantization-large/) — Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, et al. · arXiv:2308.13137 - [On Old English swa 'so'](https://md.research.elamur.ai/free/en/papers/beck-2023-old-english-em-swa-em/) — Sigrid Beck · English - [On Quick Kisses and How to Make Them Count: A Study on Event Construal in Light Verb Constructions with BERT](https://md.research.elamur.ai/free/en/papers/liu-2023-quick-kisses-how-make-them/) — Chenxin Liu, Emmanuele Chersoni · Proceedings of the 6th BlackboxNLP Workshop… - [On Second Thought, Let's Not Think Step by Step! 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